Overview

Dataset statistics

Number of variables37
Number of observations210721
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.7 MiB
Average record size in memory287.0 B

Variable types

Numeric23
Categorical11
Boolean3

Alerts

age_is_na has constant value "False" Constant
first_affiliate_tracked_is_na has constant value "False" Constant
first_browser has a high cardinality: 52 distinct values High cardinality
df_index is highly correlated with first_active_account_year and 1 other fieldsHigh correlation
first_booking_day_of_week is highly correlated with first_booking_day and 3 other fieldsHigh correlation
first_active_account_day_of_week is highly correlated with account_creation_day_of_weekHigh correlation
account_creation_day_of_week is highly correlated with first_active_account_day_of_weekHigh correlation
first_booking_day is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
first_active_account_day is highly correlated with account_creation_dayHigh correlation
account_creation_day is highly correlated with first_active_account_dayHigh correlation
first_booking_month is highly correlated with first_booking_day_of_year and 1 other fieldsHigh correlation
first_active_account_month is highly correlated with account_creation_month and 4 other fieldsHigh correlation
account_creation_month is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
first_booking_year is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
first_active_account_year is highly correlated with df_index and 1 other fieldsHigh correlation
account_creation_year is highly correlated with df_index and 1 other fieldsHigh correlation
first_booking_day_of_year is highly correlated with first_booking_month and 1 other fieldsHigh correlation
first_active_account_day_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
account_creation_day_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
first_booking_week_of_year is highly correlated with first_booking_month and 1 other fieldsHigh correlation
first_active_account_week_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
account_creation_week_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
days_account_creation_first_booking is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
days_first_active_first_booking is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
df_index is highly correlated with first_active_account_year and 1 other fieldsHigh correlation
first_booking_day_of_week is highly correlated with first_booking_day and 3 other fieldsHigh correlation
first_active_account_day_of_week is highly correlated with account_creation_day_of_weekHigh correlation
account_creation_day_of_week is highly correlated with first_active_account_day_of_weekHigh correlation
first_booking_day is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
first_active_account_day is highly correlated with account_creation_dayHigh correlation
account_creation_day is highly correlated with first_active_account_dayHigh correlation
first_booking_month is highly correlated with first_booking_day_of_year and 1 other fieldsHigh correlation
first_active_account_month is highly correlated with account_creation_month and 4 other fieldsHigh correlation
account_creation_month is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
first_booking_year is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
first_active_account_year is highly correlated with df_index and 1 other fieldsHigh correlation
account_creation_year is highly correlated with df_index and 1 other fieldsHigh correlation
first_booking_day_of_year is highly correlated with first_booking_month and 1 other fieldsHigh correlation
first_active_account_day_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
account_creation_day_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
first_booking_week_of_year is highly correlated with first_booking_month and 1 other fieldsHigh correlation
first_active_account_week_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
account_creation_week_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
days_account_creation_first_booking is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
days_first_active_first_booking is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
df_index is highly correlated with first_active_account_year and 1 other fieldsHigh correlation
first_booking_day_of_week is highly correlated with first_booking_day and 3 other fieldsHigh correlation
first_active_account_day_of_week is highly correlated with account_creation_day_of_weekHigh correlation
account_creation_day_of_week is highly correlated with first_active_account_day_of_weekHigh correlation
first_booking_day is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
first_active_account_day is highly correlated with account_creation_dayHigh correlation
account_creation_day is highly correlated with first_active_account_dayHigh correlation
first_booking_month is highly correlated with first_booking_day_of_year and 1 other fieldsHigh correlation
first_active_account_month is highly correlated with account_creation_month and 4 other fieldsHigh correlation
account_creation_month is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
first_booking_year is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
first_active_account_year is highly correlated with df_index and 1 other fieldsHigh correlation
account_creation_year is highly correlated with df_index and 1 other fieldsHigh correlation
first_booking_day_of_year is highly correlated with first_booking_month and 1 other fieldsHigh correlation
first_active_account_day_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
account_creation_day_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
first_booking_week_of_year is highly correlated with first_booking_month and 1 other fieldsHigh correlation
first_active_account_week_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
account_creation_week_of_year is highly correlated with first_active_account_month and 4 other fieldsHigh correlation
days_account_creation_first_booking is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
days_first_active_first_booking is highly correlated with first_booking_day_of_week and 3 other fieldsHigh correlation
first_device_type is highly correlated with first_browser and 3 other fieldsHigh correlation
first_browser is highly correlated with first_device_type and 2 other fieldsHigh correlation
age_is_na is highly correlated with first_device_type and 12 other fieldsHigh correlation
signup_method is highly correlated with age_is_na and 1 other fieldsHigh correlation
affiliate_channel is highly correlated with age_is_na and 2 other fieldsHigh correlation
language is highly correlated with age_is_na and 1 other fieldsHigh correlation
country_destination is highly correlated with age_is_na and 2 other fieldsHigh correlation
first_affiliate_tracked_is_na is highly correlated with first_device_type and 12 other fieldsHigh correlation
gender is highly correlated with age_is_na and 1 other fieldsHigh correlation
affiliate_provider is highly correlated with age_is_na and 2 other fieldsHigh correlation
account_creation_year is highly correlated with age_is_na and 1 other fieldsHigh correlation
signup_app is highly correlated with first_device_type and 2 other fieldsHigh correlation
first_affiliate_tracked is highly correlated with age_is_na and 1 other fieldsHigh correlation
date_first_booking_is_na is highly correlated with age_is_na and 2 other fieldsHigh correlation
df_index is highly correlated with first_booking_month and 13 other fieldsHigh correlation
gender is highly correlated with ageHigh correlation
age is highly correlated with genderHigh correlation
signup_flow is highly correlated with affiliate_channel and 4 other fieldsHigh correlation
affiliate_channel is highly correlated with signup_flow and 3 other fieldsHigh correlation
affiliate_provider is highly correlated with signup_flow and 2 other fieldsHigh correlation
first_affiliate_tracked is highly correlated with affiliate_channel and 2 other fieldsHigh correlation
signup_app is highly correlated with signup_flow and 3 other fieldsHigh correlation
first_device_type is highly correlated with signup_flow and 3 other fieldsHigh correlation
first_browser is highly correlated with signup_flow and 2 other fieldsHigh correlation
country_destination is highly correlated with date_first_booking_is_na and 8 other fieldsHigh correlation
date_first_booking_is_na is highly correlated with country_destination and 8 other fieldsHigh correlation
first_booking_day_of_week is highly correlated with country_destination and 8 other fieldsHigh correlation
first_active_account_day_of_week is highly correlated with account_creation_day_of_weekHigh correlation
account_creation_day_of_week is highly correlated with first_active_account_day_of_weekHigh correlation
first_booking_day is highly correlated with country_destination and 10 other fieldsHigh correlation
first_active_account_day is highly correlated with first_booking_day and 1 other fieldsHigh correlation
account_creation_day is highly correlated with first_booking_day and 1 other fieldsHigh correlation
first_booking_month is highly correlated with df_index and 15 other fieldsHigh correlation
first_active_account_month is highly correlated with df_index and 12 other fieldsHigh correlation
account_creation_month is highly correlated with df_index and 12 other fieldsHigh correlation
first_booking_year is highly correlated with df_index and 11 other fieldsHigh correlation
first_active_account_year is highly correlated with df_index and 10 other fieldsHigh correlation
account_creation_year is highly correlated with df_index and 10 other fieldsHigh correlation
first_booking_day_of_year is highly correlated with df_index and 15 other fieldsHigh correlation
first_active_account_day_of_year is highly correlated with df_index and 12 other fieldsHigh correlation
account_creation_day_of_year is highly correlated with df_index and 12 other fieldsHigh correlation
first_booking_week_of_year is highly correlated with df_index and 15 other fieldsHigh correlation
first_active_account_week_of_year is highly correlated with df_index and 12 other fieldsHigh correlation
account_creation_week_of_year is highly correlated with df_index and 12 other fieldsHigh correlation
days_account_creation_first_booking is highly correlated with df_index and 17 other fieldsHigh correlation
days_first_active_first_booking is highly correlated with df_index and 17 other fieldsHigh correlation
days_first_active_acount_creation is highly skewed (γ1 = -74.02857652) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
signup_flow has 162507 (77.1%) zeros Zeros
first_booking_day_of_week has 135773 (64.4%) zeros Zeros
first_active_account_day_of_week has 32472 (15.4%) zeros Zeros
account_creation_day_of_week has 32469 (15.4%) zeros Zeros
days_account_creation_first_booking has 21074 (10.0%) zeros Zeros
days_first_active_acount_creation has 210574 (99.9%) zeros Zeros
days_first_active_first_booking has 14516 (6.9%) zeros Zeros

Reproduction

Analysis started2022-02-01 16:26:04.042909
Analysis finished2022-02-01 16:27:20.184590
Duration1 minute and 16.14 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct210721
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106869.7705
Minimum0
Maximum213450
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:20.239643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10757
Q153508
median106930
Q3160245
95-th percentile202832
Maximum213450
Range213450
Interquartile range (IQR)106737

Descriptive statistics

Standard deviation61611.551
Coefficient of variation (CV)0.576510558
Kurtosis-1.200067066
Mean106869.7705
Median Absolute Deviation (MAD)53369
Skewness-0.001846801687
Sum2.25197049 × 1010
Variance3795983217
MonotonicityStrictly increasing
2022-02-01T13:27:20.336878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
1424591
 
< 0.1%
1424491
 
< 0.1%
1424501
 
< 0.1%
1424511
 
< 0.1%
1424521
 
< 0.1%
1424531
 
< 0.1%
1424541
 
< 0.1%
1424551
 
< 0.1%
1424561
 
< 0.1%
Other values (210711)210711
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
31
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
2134501
< 0.1%
2134491
< 0.1%
2134481
< 0.1%
2134471
< 0.1%
2134461
< 0.1%
2134451
< 0.1%
2134441
< 0.1%
2134431
< 0.1%
2134421
< 0.1%
2134411
< 0.1%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
-unknown-
94949 
FEMALE
61895 
MALE
53599 
OTHER
 
278

Length

Max length9
Median length6
Mean length6.841733857
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-unknown-
2nd rowMALE
3rd rowFEMALE
4th row-unknown-
5th rowFEMALE

Common Values

ValueCountFrequency (%)
-unknown-94949
45.1%
FEMALE61895
29.4%
MALE53599
25.4%
OTHER278
 
0.1%

Length

2022-02-01T13:27:20.417398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T13:27:20.463399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown94949
45.1%
female61895
29.4%
male53599
25.4%
other278
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.44355807
Minimum18
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:20.524349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q132
median34
Q335
95-th percentile55
Maximum90
Range72
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.820051231
Coefficient of variation (CV)0.2488477938
Kurtosis4.646812788
Mean35.44355807
Median Absolute Deviation (MAD)2
Skewness1.802229067
Sum7468702
Variance77.79330371
MonotonicityNot monotonic
2022-02-01T13:27:20.610497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3493015
44.1%
306123
 
2.9%
316016
 
2.9%
295963
 
2.8%
285939
 
2.8%
325855
 
2.8%
275738
 
2.7%
335526
 
2.6%
265044
 
2.4%
354858
 
2.3%
Other values (63)66644
31.6%
ValueCountFrequency (%)
18669
 
0.3%
191102
 
0.5%
20540
 
0.3%
21982
 
0.5%
221702
 
0.8%
232462
1.2%
243220
1.5%
254458
2.1%
265044
2.4%
275738
2.7%
ValueCountFrequency (%)
9018
< 0.1%
8913
< 0.1%
8812
 
< 0.1%
8731
< 0.1%
8627
< 0.1%
8526
< 0.1%
8420
< 0.1%
8325
< 0.1%
8226
< 0.1%
8130
< 0.1%

signup_method
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
basic
150954 
facebook
59222 
google
 
545

Length

Max length8
Median length5
Mean length5.84572017
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowfacebook
3rd rowfacebook
4th rowbasic
5th rowbasic

Common Values

ValueCountFrequency (%)
basic150954
71.6%
facebook59222
 
28.1%
google545
 
0.3%

Length

2022-02-01T13:27:20.694966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T13:27:20.743961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
basic150954
71.6%
facebook59222
 
28.1%
google545
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

signup_flow
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.285021426
Minimum0
Maximum25
Zeros162507
Zeros (%)77.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:20.787480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.657414612
Coefficient of variation (CV)2.331009031
Kurtosis3.278878961
Mean3.285021426
Median Absolute Deviation (MAD)0
Skewness2.224659025
Sum692223
Variance58.63599854
MonotonicityNot monotonic
2022-02-01T13:27:20.855342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0162507
77.1%
2514579
 
6.9%
129248
 
4.4%
38718
 
4.1%
26744
 
3.2%
244283
 
2.0%
232819
 
1.3%
11026
 
0.5%
6296
 
0.1%
8238
 
0.1%
Other values (7)263
 
0.1%
ValueCountFrequency (%)
0162507
77.1%
11026
 
0.5%
26744
 
3.2%
38718
 
4.1%
41
 
< 0.1%
535
 
< 0.1%
6296
 
0.1%
8238
 
0.1%
102
 
< 0.1%
129248
 
4.4%
ValueCountFrequency (%)
2514579
6.9%
244283
 
2.0%
232819
 
1.3%
21190
 
0.1%
2014
 
< 0.1%
1611
 
< 0.1%
1510
 
< 0.1%
129248
4.4%
102
 
< 0.1%
8238
 
0.1%

language
Categorical

HIGH CORRELATION

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
en
203692 
zh
 
1623
fr
 
1136
es
 
902
ko
 
738
Other values (20)
 
2630

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en203692
96.7%
zh1623
 
0.8%
fr1136
 
0.5%
es902
 
0.4%
ko738
 
0.4%
de728
 
0.3%
it503
 
0.2%
ru379
 
0.2%
pt235
 
0.1%
ja223
 
0.1%
Other values (15)562
 
0.3%

Length

2022-02-01T13:27:20.928097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en203692
96.7%
zh1623
 
0.8%
fr1136
 
0.5%
es902
 
0.4%
ko738
 
0.4%
de728
 
0.3%
it503
 
0.2%
ru379
 
0.2%
pt235
 
0.1%
ja223
 
0.1%
Other values (15)562
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

affiliate_channel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
direct
136034 
sem-brand
25679 
sem-non-brand
18582 
other
 
8810
seo
 
8542
Other values (3)
 
13074

Length

Max length13
Median length6
Mean length6.748311749
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowseo
3rd rowdirect
4th rowother
5th rowother

Common Values

ValueCountFrequency (%)
direct136034
64.6%
sem-brand25679
 
12.2%
sem-non-brand18582
 
8.8%
other8810
 
4.2%
seo8542
 
4.1%
api8095
 
3.8%
content3900
 
1.9%
remarketing1079
 
0.5%

Length

2022-02-01T13:27:20.995378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T13:27:21.047088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
direct136034
64.6%
sem-brand25679
 
12.2%
sem-non-brand18582
 
8.8%
other8810
 
4.2%
seo8542
 
4.1%
api8095
 
3.8%
content3900
 
1.9%
remarketing1079
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

affiliate_provider
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
direct
135738 
google
50957 
other
 
12400
craigslist
 
3406
bing
 
2299
Other values (13)
 
5921

Length

Max length19
Median length6
Mean length6.039331628
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowdirect
2nd rowgoogle
3rd rowdirect
4th rowother
5th rowcraigslist

Common Values

ValueCountFrequency (%)
direct135738
64.4%
google50957
 
24.2%
other12400
 
5.9%
craigslist3406
 
1.6%
bing2299
 
1.1%
facebook2253
 
1.1%
vast821
 
0.4%
padmapper757
 
0.4%
facebook-open-graph538
 
0.3%
yahoo491
 
0.2%
Other values (8)1061
 
0.5%

Length

2022-02-01T13:27:21.120701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direct135738
64.4%
google50957
 
24.2%
other12400
 
5.9%
craigslist3406
 
1.6%
bing2299
 
1.1%
facebook2253
 
1.1%
vast821
 
0.4%
padmapper757
 
0.4%
facebook-open-graph538
 
0.3%
yahoo491
 
0.2%
Other values (8)1061
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_affiliate_tracked
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
untracked
107916 
linked
45648 
omg
43421 
tracked-other
 
6045
unknown
 
5990
Other values (3)
 
1701

Length

Max length13
Median length9
Mean length7.157146179
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowomg
5th rowuntracked

Common Values

ValueCountFrequency (%)
untracked107916
51.2%
linked45648
21.7%
omg43421
20.6%
tracked-other6045
 
2.9%
unknown5990
 
2.8%
product1529
 
0.7%
marketing139
 
0.1%
local ops33
 
< 0.1%

Length

2022-02-01T13:27:21.192277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T13:27:21.241749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
untracked107916
51.2%
linked45648
21.7%
omg43421
20.6%
tracked-other6045
 
2.9%
unknown5990
 
2.8%
product1529
 
0.7%
marketing139
 
0.1%
local33
 
< 0.1%
ops33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

signup_app
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Web
180206 
iOS
18898 
Moweb
 
6193
Android
 
5424

Length

Max length7
Median length3
Mean length3.161739931
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb

Common Values

ValueCountFrequency (%)
Web180206
85.5%
iOS18898
 
9.0%
Moweb6193
 
2.9%
Android5424
 
2.6%

Length

2022-02-01T13:27:21.321925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T13:27:21.374990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
web180206
85.5%
ios18898
 
9.0%
moweb6193
 
2.9%
android5424
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_device_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Mac Desktop
88350 
Windows Desktop
71737 
iPhone
20601 
iPad
14170 
Other/Unknown
10560 
Other values (4)
 
5303

Length

Max length18
Median length11
Mean length11.57179873
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMac Desktop
2nd rowMac Desktop
3rd rowMac Desktop
4th rowMac Desktop
5th rowMac Desktop

Common Values

ValueCountFrequency (%)
Mac Desktop88350
41.9%
Windows Desktop71737
34.0%
iPhone20601
 
9.8%
iPad14170
 
6.7%
Other/Unknown10560
 
5.0%
Android Phone2775
 
1.3%
Android Tablet1270
 
0.6%
Desktop (Other)1183
 
0.6%
SmartPhone (Other)75
 
< 0.1%

Length

2022-02-01T13:27:21.427842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T13:27:21.480336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
desktop161270
42.9%
mac88350
23.5%
windows71737
19.1%
iphone20601
 
5.5%
ipad14170
 
3.8%
other/unknown10560
 
2.8%
android4045
 
1.1%
phone2775
 
0.7%
tablet1270
 
0.3%
other1258
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_browser
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Chrome
63045 
Safari
44502 
Firefox
33186 
-unknown-
27056 
IE
20760 
Other values (47)
22172 

Length

Max length20
Median length6
Mean length6.87365284
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowChrome
2nd rowChrome
3rd rowFirefox
4th rowChrome
5th rowSafari

Common Values

ValueCountFrequency (%)
Chrome63045
29.9%
Safari44502
21.1%
Firefox33186
15.7%
-unknown-27056
12.8%
IE20760
 
9.9%
Mobile Safari19053
 
9.0%
Chrome Mobile1251
 
0.6%
Android Browser838
 
0.4%
AOL Explorer238
 
0.1%
Opera183
 
0.1%
Other values (42)609
 
0.3%

Length

2022-02-01T13:27:21.558270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome64296
27.7%
safari63555
27.4%
firefox33215
14.3%
unknown27056
11.6%
ie20796
 
9.0%
mobile20371
 
8.8%
browser901
 
0.4%
android838
 
0.4%
explorer271
 
0.1%
aol238
 
0.1%
Other values (48)786
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

country_destination
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
NDF
123233 
US
61382 
other
 
9920
FR
 
4960
IT
 
2783
Other values (7)
 
8443

Length

Max length5
Median length3
Mean length2.72604534
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNDF
2nd rowNDF
3rd rowother
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
NDF123233
58.5%
US61382
29.1%
other9920
 
4.7%
FR4960
 
2.4%
IT2783
 
1.3%
GB2284
 
1.1%
ES2218
 
1.1%
CA1404
 
0.7%
DE1042
 
0.5%
NL750
 
0.4%
Other values (2)745
 
0.4%

Length

2022-02-01T13:27:21.632910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndf123233
58.5%
us61382
29.1%
other9920
 
4.7%
fr4960
 
2.4%
it2783
 
1.3%
gb2284
 
1.1%
es2218
 
1.1%
ca1404
 
0.7%
de1042
 
0.5%
nl750
 
0.4%
Other values (2)745
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date_first_booking_is_na
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size205.9 KiB
True
123233 
False
87488 
ValueCountFrequency (%)
True123233
58.5%
False87488
41.5%
2022-02-01T13:27:21.680357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

age_is_na
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size205.9 KiB
False
210721 
ValueCountFrequency (%)
False210721
100.0%
2022-02-01T13:27:21.702780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

first_affiliate_tracked_is_na
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size205.9 KiB
False
210721 
ValueCountFrequency (%)
False210721
100.0%
2022-02-01T13:27:21.721761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

first_booking_day_of_week
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.163562246
Minimum0
Maximum6
Zeros135773
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:21.754600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.852311685
Coefficient of variation (CV)1.591931752
Kurtosis0.539268494
Mean1.163562246
Median Absolute Deviation (MAD)0
Skewness1.383228706
Sum245187
Variance3.431058578
MonotonicityNot monotonic
2022-02-01T13:27:21.811272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0135773
64.4%
214177
 
6.7%
114060
 
6.7%
313702
 
6.5%
413065
 
6.2%
510257
 
4.9%
69687
 
4.6%
ValueCountFrequency (%)
0135773
64.4%
114060
 
6.7%
214177
 
6.7%
313702
 
6.5%
413065
 
6.2%
510257
 
4.9%
69687
 
4.6%
ValueCountFrequency (%)
69687
 
4.6%
510257
 
4.9%
413065
 
6.2%
313702
 
6.5%
214177
 
6.7%
114060
 
6.7%
0135773
64.4%

first_active_account_day_of_week
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.763407539
Minimum0
Maximum6
Zeros32472
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:21.872675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.944705547
Coefficient of variation (CV)0.7037346173
Kurtosis-1.150393083
Mean2.763407539
Median Absolute Deviation (MAD)2
Skewness0.1668700811
Sum582308
Variance3.781879664
MonotonicityNot monotonic
2022-02-01T13:27:21.928299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
134612
16.4%
233687
16.0%
032472
15.4%
332163
15.3%
429346
13.9%
624244
11.5%
524197
11.5%
ValueCountFrequency (%)
032472
15.4%
134612
16.4%
233687
16.0%
332163
15.3%
429346
13.9%
524197
11.5%
624244
11.5%
ValueCountFrequency (%)
624244
11.5%
524197
11.5%
429346
13.9%
332163
15.3%
233687
16.0%
134612
16.4%
032472
15.4%

account_creation_day_of_week
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.763450249
Minimum0
Maximum6
Zeros32469
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:21.990014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.944704642
Coefficient of variation (CV)0.7037234133
Kurtosis-1.150420741
Mean2.763450249
Median Absolute Deviation (MAD)2
Skewness0.1668553777
Sum582317
Variance3.781876144
MonotonicityNot monotonic
2022-02-01T13:27:22.050332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
134615
16.4%
233687
16.0%
032469
15.4%
332158
15.3%
429351
13.9%
624245
11.5%
524196
11.5%
ValueCountFrequency (%)
032469
15.4%
134615
16.4%
233687
16.0%
332158
15.3%
429351
13.9%
524196
11.5%
624245
11.5%
ValueCountFrequency (%)
624245
11.5%
524196
11.5%
429351
13.9%
332158
15.3%
233687
16.0%
134615
16.4%
032469
15.4%

first_booking_day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.45567362
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:22.126588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q119
median29
Q329
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.655450281
Coefficient of variation (CV)0.3690130764
Kurtosis0.1812988587
Mean23.45567362
Median Absolute Deviation (MAD)0
Skewness-1.283625481
Sum4942603
Variance74.91681957
MonotonicityNot monotonic
2022-02-01T13:27:22.200407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
29125816
59.7%
103046
 
1.4%
173016
 
1.4%
112999
 
1.4%
132973
 
1.4%
162971
 
1.4%
152968
 
1.4%
52942
 
1.4%
32913
 
1.4%
62909
 
1.4%
Other values (21)58168
27.6%
ValueCountFrequency (%)
12720
1.3%
22828
1.3%
32913
1.4%
42815
1.3%
52942
1.4%
62909
1.4%
72881
1.4%
82901
1.4%
92873
1.4%
103046
1.4%
ValueCountFrequency (%)
311535
 
0.7%
302660
 
1.3%
29125816
59.7%
282839
 
1.3%
272722
 
1.3%
262777
 
1.3%
252835
 
1.3%
242842
 
1.3%
232808
 
1.3%
222899
 
1.4%

first_active_account_day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.87166443
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:22.280553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.740370127
Coefficient of variation (CV)0.5506902043
Kurtosis-1.186209018
Mean15.87166443
Median Absolute Deviation (MAD)8
Skewness-0.01164470356
Sum3344493
Variance76.39406996
MonotonicityNot monotonic
2022-02-01T13:27:22.356606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
247317
 
3.5%
167162
 
3.4%
187160
 
3.4%
207130
 
3.4%
237122
 
3.4%
197109
 
3.4%
287056
 
3.3%
267038
 
3.3%
137037
 
3.3%
177035
 
3.3%
Other values (21)139555
66.2%
ValueCountFrequency (%)
16125
2.9%
26707
3.2%
36853
3.3%
46748
3.2%
56932
3.3%
66875
3.3%
76648
3.2%
86827
3.2%
96832
3.2%
106941
3.3%
ValueCountFrequency (%)
313695
1.8%
306675
3.2%
296485
3.1%
287056
3.3%
276979
3.3%
267038
3.3%
256877
3.3%
247317
3.5%
237122
3.4%
226866
3.3%

account_creation_day
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.87128478
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:22.445193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.740640362
Coefficient of variation (CV)0.5507204037
Kurtosis-1.186274197
Mean15.87128478
Median Absolute Deviation (MAD)8
Skewness-0.01166694181
Sum3344413
Variance76.39879393
MonotonicityNot monotonic
2022-02-01T13:27:22.524028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
247318
 
3.5%
167168
 
3.4%
187160
 
3.4%
207131
 
3.4%
237121
 
3.4%
197105
 
3.4%
287059
 
3.3%
267035
 
3.3%
137033
 
3.3%
177033
 
3.3%
Other values (21)139558
66.2%
ValueCountFrequency (%)
16126
2.9%
26709
3.2%
36858
3.3%
46747
3.2%
56934
3.3%
66874
3.3%
76646
3.2%
86830
3.2%
96831
3.2%
106939
3.3%
ValueCountFrequency (%)
313693
1.8%
306675
3.2%
296487
3.1%
287059
3.3%
276977
3.3%
267035
3.3%
256874
3.3%
247318
3.5%
237121
3.4%
226872
3.3%

first_booking_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.047356457
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:22.598364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median6
Q36
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.04984678
Coefficient of variation (CV)0.3389657604
Kurtosis1.918530764
Mean6.047356457
Median Absolute Deviation (MAD)0
Skewness0.3903313435
Sum1274305
Variance4.20187182
MonotonicityNot monotonic
2022-02-01T13:27:22.660144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6133595
63.4%
510328
 
4.9%
48667
 
4.1%
38249
 
3.9%
77141
 
3.4%
86938
 
3.3%
26653
 
3.2%
96482
 
3.1%
16372
 
3.0%
106094
 
2.9%
Other values (2)10202
 
4.8%
ValueCountFrequency (%)
16372
 
3.0%
26653
 
3.2%
38249
 
3.9%
48667
 
4.1%
510328
 
4.9%
6133595
63.4%
77141
 
3.4%
86938
 
3.3%
96482
 
3.1%
106094
 
2.9%
ValueCountFrequency (%)
125011
 
2.4%
115191
 
2.5%
106094
 
2.9%
96482
 
3.1%
86938
 
3.3%
77141
 
3.4%
6133595
63.4%
510328
 
4.9%
48667
 
4.1%
38249
 
3.9%

first_active_account_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.023153838
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:22.733248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.236360926
Coefficient of variation (CV)0.5373199843
Kurtosis-0.9694602136
Mean6.023153838
Median Absolute Deviation (MAD)3
Skewness0.2485199619
Sum1269205
Variance10.47403204
MonotonicityNot monotonic
2022-02-01T13:27:22.794083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
627129
12.9%
525601
12.1%
421603
10.3%
320060
9.5%
117261
8.2%
216336
7.8%
915181
7.2%
814395
6.8%
713627
6.5%
1013323
6.3%
Other values (2)26205
12.4%
ValueCountFrequency (%)
117261
8.2%
216336
7.8%
320060
9.5%
421603
10.3%
525601
12.1%
627129
12.9%
713627
6.5%
814395
6.8%
915181
7.2%
1013323
6.3%
ValueCountFrequency (%)
1213238
6.3%
1112967
6.2%
1013323
6.3%
915181
7.2%
814395
6.8%
713627
6.5%
627129
12.9%
525601
12.1%
421603
10.3%
320060
9.5%

account_creation_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.02316333
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:22.859562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.236538282
Coefficient of variation (CV)0.5373485832
Kurtosis-0.9695745673
Mean6.02316333
Median Absolute Deviation (MAD)3
Skewness0.2484757975
Sum1269207
Variance10.47518005
MonotonicityNot monotonic
2022-02-01T13:27:22.920773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
627128
12.9%
525604
12.2%
421595
10.2%
320059
9.5%
117266
8.2%
216338
7.8%
915186
7.2%
814392
6.8%
713623
6.5%
1013323
6.3%
Other values (2)26207
12.4%
ValueCountFrequency (%)
117266
8.2%
216338
7.8%
320059
9.5%
421595
10.2%
525604
12.2%
627128
12.9%
713623
6.5%
814392
6.8%
915186
7.2%
1013323
6.3%
ValueCountFrequency (%)
1213242
6.3%
1112965
6.2%
1013323
6.3%
915186
7.2%
814392
6.8%
713623
6.5%
627128
12.9%
525604
12.2%
421595
10.2%
320059
9.5%

first_booking_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.187585
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:22.980449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2012
Q12013
median2015
Q32015
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.165490157
Coefficient of variation (CV)0.0005786403242
Kurtosis0.8803769206
Mean2014.187585
Median Absolute Deviation (MAD)0
Skewness-1.322180239
Sum424431622
Variance1.358367306
MonotonicityNot monotonic
2022-02-01T13:27:23.041959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2015124980
59.3%
201431925
 
15.2%
201330712
 
14.6%
201216013
 
7.6%
20115650
 
2.7%
20101441
 
0.7%
ValueCountFrequency (%)
20101441
 
0.7%
20115650
 
2.7%
201216013
 
7.6%
201330712
 
14.6%
201431925
 
15.2%
2015124980
59.3%
ValueCountFrequency (%)
2015124980
59.3%
201431925
 
15.2%
201330712
 
14.6%
201216013
 
7.6%
20115650
 
2.7%
20101441
 
0.7%

first_active_account_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.02537
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:23.107160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9375234033
Coefficient of variation (CV)0.0004657285583
Kurtosis0.2805745293
Mean2013.02537
Median Absolute Deviation (MAD)1
Skewness-0.8265063759
Sum424186719
Variance0.8789501318
MonotonicityIncreasing
2022-02-01T13:27:23.174032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
201381880
38.9%
201475606
35.9%
201238923
18.5%
201111602
 
5.5%
20102707
 
1.3%
20093
 
< 0.1%
ValueCountFrequency (%)
20093
 
< 0.1%
20102707
 
1.3%
201111602
 
5.5%
201238923
18.5%
201381880
38.9%
201475606
35.9%
ValueCountFrequency (%)
201475606
35.9%
201381880
38.9%
201238923
18.5%
201111602
 
5.5%
20102707
 
1.3%
20093
 
< 0.1%

account_creation_year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2013
81890 
2014
75642 
2012
38905 
2011
11582 
2010
 
2702

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010
2nd row2011
3rd row2011
4th row2010
5th row2010

Common Values

ValueCountFrequency (%)
201381890
38.9%
201475642
35.9%
201238905
18.5%
201111582
 
5.5%
20102702
 
1.3%

Length

2022-02-01T13:27:23.244083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T13:27:23.292812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
201381890
38.9%
201475642
35.9%
201238905
18.5%
201111582
 
5.5%
20102702
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_booking_day_of_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct366
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.0442623
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:23.358698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q1180
median180
Q3180
95-th percentile303
Maximum366
Range365
Interquartile range (IQR)0

Descriptive statistics

Standard deviation62.68708613
Coefficient of variation (CV)0.3560870733
Kurtosis1.777671783
Mean176.0442623
Median Absolute Deviation (MAD)0
Skewness0.04137439194
Sum37096223
Variance3929.670768
MonotonicityNot monotonic
2022-02-01T13:27:23.455289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180123533
58.6%
162410
 
0.2%
142410
 
0.2%
141407
 
0.2%
176401
 
0.2%
134394
 
0.2%
164384
 
0.2%
170382
 
0.2%
157379
 
0.2%
161375
 
0.2%
Other values (356)83646
39.7%
ValueCountFrequency (%)
186
 
< 0.1%
2145
0.1%
3192
0.1%
4181
0.1%
5170
0.1%
6187
0.1%
7218
0.1%
8173
0.1%
9209
0.1%
10234
0.1%
ValueCountFrequency (%)
36635
 
< 0.1%
365165
0.1%
364161
0.1%
363141
0.1%
362156
0.1%
361151
0.1%
360101
< 0.1%
359118
0.1%
358108
0.1%
357142
0.1%

first_active_account_day_of_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct366
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.9508402
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:23.552238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q189
median157
Q3248
95-th percentile341
Maximum366
Range365
Interquartile range (IQR)159

Descriptive statistics

Standard deviation98.82570567
Coefficient of variation (CV)0.5884204303
Kurtosis-0.9640146458
Mean167.9508402
Median Absolute Deviation (MAD)78
Skewness0.2556678312
Sum35390769
Variance9766.520101
MonotonicityNot monotonic
2022-02-01T13:27:23.640329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1751049
 
0.5%
1761041
 
0.5%
1781009
 
0.5%
1771004
 
0.5%
1331001
 
0.5%
134989
 
0.5%
154986
 
0.5%
170983
 
0.5%
162972
 
0.5%
141966
 
0.5%
Other values (356)200721
95.3%
ValueCountFrequency (%)
1257
 
0.1%
2524
0.2%
3565
0.3%
4474
0.2%
5496
0.2%
6524
0.2%
7564
0.3%
8578
0.3%
9650
0.3%
10612
0.3%
ValueCountFrequency (%)
36699
 
< 0.1%
365409
0.2%
364500
0.2%
363440
0.2%
362501
0.2%
361455
0.2%
360355
0.2%
359251
0.1%
358317
0.2%
357380
0.2%

account_creation_day_of_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct366
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.9507548
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:23.735457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q189
median157
Q3248
95-th percentile341
Maximum366
Range365
Interquartile range (IQR)159

Descriptive statistics

Standard deviation98.83110853
Coefficient of variation (CV)0.5884528989
Kurtosis-0.964153136
Mean167.9507548
Median Absolute Deviation (MAD)78
Skewness0.2556023476
Sum35390751
Variance9767.588012
MonotonicityNot monotonic
2022-02-01T13:27:23.824561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1751049
 
0.5%
1761042
 
0.5%
1781008
 
0.5%
1771003
 
0.5%
1331001
 
0.5%
134989
 
0.5%
154986
 
0.5%
170983
 
0.5%
162973
 
0.5%
141965
 
0.5%
Other values (356)200722
95.3%
ValueCountFrequency (%)
1256
 
0.1%
2523
0.2%
3568
0.3%
4474
0.2%
5497
0.2%
6524
0.2%
7564
0.3%
8579
0.3%
9649
0.3%
10613
0.3%
ValueCountFrequency (%)
36698
 
< 0.1%
365409
0.2%
364500
0.2%
363440
0.2%
362502
0.2%
361455
0.2%
360355
0.2%
359250
0.1%
358317
0.2%
357380
0.2%

first_booking_week_of_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.0757542
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-02-01T13:27:23.917590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q127
median27
Q327
95-th percentile44
Maximum53
Range52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.943092287
Coefficient of variation (CV)0.3429658149
Kurtosis1.711245047
Mean26.0757542
Median Absolute Deviation (MAD)0
Skewness-0.1392491066
Sum5494709
Variance79.97889966
MonotonicityNot monotonic
2022-02-01T13:27:24.007919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27125057
59.3%
262499
 
1.2%
242469
 
1.2%
212446
 
1.2%
202420
 
1.1%
252393
 
1.1%
232379
 
1.1%
182289
 
1.1%
192276
 
1.1%
222219
 
1.1%
Other values (43)64274
30.5%
ValueCountFrequency (%)
11098
0.5%
21444
0.7%
31723
0.8%
41411
0.7%
51458
0.7%
61592
0.8%
71719
0.8%
81692
0.8%
91762
0.8%
101849
0.9%
ValueCountFrequency (%)
531
 
< 0.1%
52932
0.4%
511150
0.5%
501184
0.6%
491262
0.6%
481139
0.5%
471137
0.5%
461213
0.6%
451372
0.7%
441209
0.6%

first_active_account_week_of_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.39574129
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-02-01T13:27:24.097054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median23
Q336
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.01829598
Coefficient of variation (CV)0.5746206198
Kurtosis-0.9570199261
Mean24.39574129
Median Absolute Deviation (MAD)11
Skewness0.2497096149
Sum5140695
Variance196.5126221
MonotonicityNot monotonic
2022-02-01T13:27:24.188656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266803
 
3.2%
256445
 
3.1%
246183
 
2.9%
216121
 
2.9%
236073
 
2.9%
206070
 
2.9%
225654
 
2.7%
195511
 
2.6%
185449
 
2.6%
175364
 
2.5%
Other values (43)151048
71.7%
ValueCountFrequency (%)
13292
1.6%
23946
1.9%
34145
2.0%
43911
1.9%
53884
1.8%
64015
1.9%
73973
1.9%
84170
2.0%
94405
2.1%
104363
2.1%
ValueCountFrequency (%)
533
 
< 0.1%
522777
1.3%
512863
1.4%
502984
1.4%
493267
1.6%
482941
1.4%
473015
1.4%
463190
1.5%
453116
1.5%
442818
1.3%

account_creation_week_of_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.39557519
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-02-01T13:27:24.277645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median23
Q336
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.01901207
Coefficient of variation (CV)0.5746538855
Kurtosis-0.9571391421
Mean24.39557519
Median Absolute Deviation (MAD)11
Skewness0.2496500729
Sum5140660
Variance196.5326995
MonotonicityNot monotonic
2022-02-01T13:27:24.368910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266802
 
3.2%
256446
 
3.1%
246184
 
2.9%
216120
 
2.9%
236072
 
2.9%
206069
 
2.9%
225658
 
2.7%
195508
 
2.6%
185455
 
2.6%
175362
 
2.5%
Other values (43)151045
71.7%
ValueCountFrequency (%)
13295
1.6%
23948
1.9%
34145
2.0%
43910
1.9%
53885
1.8%
64015
1.9%
73976
1.9%
84170
2.0%
94405
2.1%
104363
2.1%
ValueCountFrequency (%)
533
 
< 0.1%
522776
1.3%
512863
1.4%
502985
1.4%
493270
1.6%
482940
1.4%
473018
1.4%
463186
1.5%
453117
1.5%
442818
1.3%

days_account_creation_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1967
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean432.234979
Minimum0
Maximum2001
Zeros21074
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-02-01T13:27:25.244624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median421
Q3687
95-th percentile1148
Maximum2001
Range2001
Interquartile range (IQR)681

Descriptive statistics

Standard deviation400.3373087
Coefficient of variation (CV)0.926202941
Kurtosis-0.1184298914
Mean432.234979
Median Absolute Deviation (MAD)380
Skewness0.6753168766
Sum91080987
Variance160269.9608
MonotonicityNot monotonic
2022-02-01T13:27:25.343907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021074
 
10.0%
114521
 
6.9%
26399
 
3.0%
33955
 
1.9%
42888
 
1.4%
52226
 
1.1%
61758
 
0.8%
71638
 
0.8%
81290
 
0.6%
91032
 
0.5%
Other values (1957)153940
73.1%
ValueCountFrequency (%)
021074
10.0%
114521
6.9%
26399
 
3.0%
33955
 
1.9%
42888
 
1.4%
52226
 
1.1%
61758
 
0.8%
71638
 
0.8%
81290
 
0.6%
91032
 
0.5%
ValueCountFrequency (%)
20012
< 0.1%
19992
< 0.1%
19981
 
< 0.1%
19952
< 0.1%
19941
 
< 0.1%
19923
< 0.1%
19914
< 0.1%
19902
< 0.1%
19852
< 0.1%
19821
 
< 0.1%

days_first_active_acount_creation
Real number (ℝ)

SKEWED
ZEROS

Distinct122
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2097228088
Minimum-1456
Maximum0
Zeros210574
Zeros (%)99.9%
Negative147
Negative (%)0.1%
Memory size1.6 MiB
2022-02-01T13:27:25.452571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1456
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.65651772
Coefficient of variation (CV)-55.5805913
Kurtosis6421.885233
Mean-0.2097228088
Median Absolute Deviation (MAD)0
Skewness-74.02857652
Sum-44193
Variance135.8744055
MonotonicityNot monotonic
2022-02-01T13:27:25.543217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0210574
99.9%
-15
 
< 0.1%
-24
 
< 0.1%
-53
 
< 0.1%
-73
 
< 0.1%
-293
 
< 0.1%
-6342
 
< 0.1%
-1032
 
< 0.1%
-202
 
< 0.1%
-92
 
< 0.1%
Other values (112)121
 
0.1%
ValueCountFrequency (%)
-14561
< 0.1%
-13691
< 0.1%
-13611
< 0.1%
-11481
< 0.1%
-10361
< 0.1%
-10181
< 0.1%
-10111
< 0.1%
-9981
< 0.1%
-9951
< 0.1%
-8821
< 0.1%
ValueCountFrequency (%)
0210574
99.9%
-15
 
< 0.1%
-24
 
< 0.1%
-32
 
< 0.1%
-41
 
< 0.1%
-53
 
< 0.1%
-62
 
< 0.1%
-73
 
< 0.1%
-92
 
< 0.1%
-101
 
< 0.1%

days_first_active_first_booking
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1969
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean431.4447018
Minimum-1
Maximum2292
Zeros14516
Zeros (%)6.9%
Negative21062
Negative (%)10.0%
Memory size1.6 MiB
2022-02-01T13:27:25.644512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q15
median420
Q3687
95-th percentile1147
Maximum2292
Range2293
Interquartile range (IQR)682

Descriptive statistics

Standard deviation400.468811
Coefficient of variation (CV)0.928204262
Kurtosis-0.1154122352
Mean431.4447018
Median Absolute Deviation (MAD)380
Skewness0.6758559145
Sum90914459
Variance160375.2686
MonotonicityNot monotonic
2022-02-01T13:27:25.739895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-121062
 
10.0%
014516
 
6.9%
16394
 
3.0%
23952
 
1.9%
32888
 
1.4%
42221
 
1.1%
51755
 
0.8%
61637
 
0.8%
71288
 
0.6%
81032
 
0.5%
Other values (1959)153976
73.1%
ValueCountFrequency (%)
-121062
10.0%
014516
6.9%
16394
 
3.0%
23952
 
1.9%
32888
 
1.4%
42221
 
1.1%
51755
 
0.8%
61637
 
0.8%
71288
 
0.6%
81032
 
0.5%
ValueCountFrequency (%)
22921
 
< 0.1%
22271
 
< 0.1%
20002
< 0.1%
19982
< 0.1%
19971
 
< 0.1%
19942
< 0.1%
19931
 
< 0.1%
19913
< 0.1%
19904
< 0.1%
19892
< 0.1%

Interactions

2022-02-01T13:27:15.939804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:21.302093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:23.807590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:26.700192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:29.116516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:31.496590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:33.936423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:36.194943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:38.698937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:40.939219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:43.494541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:45.733038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:47.960682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:50.181657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:52.809824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:54.997916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:57.275390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:59.505085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:02.211618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:04.756413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:07.767566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:10.603746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:13.644641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:16.030426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:21.425714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:23.971217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:26.790648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:29.209711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:31.593423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:34.028057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:36.285448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:38.791625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:41.028931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:43.596808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:45.822455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:48.049324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:50.283088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:52.899240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:55.090947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:57.368119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:59.596535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:02.312680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:04.867422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:07.872086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:10.700968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:13.738994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:16.127045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:21.615474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:24.098138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:26.887079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:29.317978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:31.692082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:34.124457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:36.383164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:38.888092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:41.124491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:43.693056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:45.917353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:48.146602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:50.379966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:52.995464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:55.189232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:57.465527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:00.151469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:02.419363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:04.983508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:07.976520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:10.804940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:13.839612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:16.218848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:21.706319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:24.270376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:26.979397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:29.417847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:31.785140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:34.216566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:36.478452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:38.992251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:41.217040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:43.787004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:46.008303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:48.236542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:50.473771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:53.087866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:55.284033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:57.559567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:00.244294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:02.523865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:05.089097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:08.095850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:10.905182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:13.936603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:16.312923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:21.832784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:24.411017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:27.075977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:29.516012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:31.886896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:34.312695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:36.575394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:39.086852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:41.316235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:43.882346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:46.102871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:48.330306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:50.569653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:53.181595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:55.383587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:57.656278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:00.340472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:02.631062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:05.295135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:08.212370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:11.006519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:14.038604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:16.407959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:21.928247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:24.513523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:27.174201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:29.625978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:31.985826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:34.413890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:36.674334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:39.192982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:41.422889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:43.979502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:46.195295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:48.425139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:51.043433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:53.275364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:55.493519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:26:57.752817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:00.437161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:02.736201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:05.405116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-01T13:27:08.317211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-01T13:27:26.210588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-01T13:27:26.387395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-01T13:27:26.531841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-01T13:27:18.319369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-01T13:27:19.233518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexgenderagesignup_methodsignup_flowlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationdate_first_booking_is_naage_is_nafirst_affiliate_tracked_is_nafirst_booking_day_of_weekfirst_active_account_day_of_weekaccount_creation_day_of_weekfirst_booking_dayfirst_active_account_dayaccount_creation_dayfirst_booking_monthfirst_active_account_monthaccount_creation_monthfirst_booking_yearfirst_active_account_yearaccount_creation_yearfirst_booking_day_of_yearfirst_active_account_day_of_yearaccount_creation_day_of_yearfirst_booking_week_of_yearfirst_active_account_week_of_yearaccount_creation_week_of_yeardays_account_creation_first_bookingdays_first_active_acount_creationdays_first_active_first_booking
00-unknown-34facebook0endirectdirectuntrackedWebMac DesktopChromeNDFyesnono030291928636201520092010180781792712261827-4662292
11MALE38facebook0enseogoogleuntrackedWebMac DesktopChromeNDFyesnono0522923256552015200920111801431452721211496-7322227
23FEMALE42facebook0endirectdirectuntrackedWebMac DesktopFirefoxothernonono550831591012201220092011252304339364449278-7651042
35-unknown-34basic0enotherotheromgWebMac DesktopChromeUSnonono544211111201020102010211535353100
46FEMALE46basic0enothercraigslistuntrackedWebMac DesktopSafariUSnonono15552211120102010201052215353302
57FEMALE47basic0endirectdirectomgWebMac DesktopSafariUSnonono26613331112010201020101333253531009
68FEMALE50basic0enothercraigslistuntrackedWebMac DesktopSafariUSnonono30029447112010201020102104430112060205
79-unknown-46basic0enothercraigslistomgWebMac DesktopFirefoxUSnonono00044411120102010201044411100-1
810FEMALE36basic0enothercraigslistuntrackedWebMac DesktopFirefoxUSnonono200644111201020102010644111201
911FEMALE47basic0enothercraigslistuntrackedWebiPhone-unknown-NDFyesnono0112955611201520102010180552711200102000

Last rows

df_indexgenderagesignup_methodsignup_flowlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationdate_first_booking_is_naage_is_nafirst_affiliate_tracked_is_nafirst_booking_day_of_weekfirst_active_account_day_of_weekaccount_creation_day_of_weekfirst_booking_dayfirst_active_account_dayaccount_creation_dayfirst_booking_monthfirst_active_account_monthaccount_creation_monthfirst_booking_yearfirst_active_account_yearaccount_creation_yearfirst_booking_day_of_yearfirst_active_account_day_of_yearaccount_creation_day_of_yearfirst_booking_week_of_yearfirst_active_account_week_of_yearaccount_creation_week_of_yeardays_account_creation_first_bookingdays_first_active_acount_creationdays_first_active_first_booking
210711213441FEMALE34basic0endirectdirectlinkedWebMac DesktopChromeESnonono20013303086620142014201422518118133272744043
210712213442-unknown-34basic0ensem-brandgoogleomgWebMac DesktopChromeNDFyesnono0002930306662015201420141801811812727273640363
210713213443FEMALE36basic0ensem-brandgooglelinkedWebMac DesktopSafariUSnonono60013303076620142014201419418118128272713012
210714213444-unknown-34basic0endirectdirectlinkedWebWindows DesktopChromeNDFyesnono0002930306662015201420141801811812727273640363
210715213445FEMALE23basic0ensem-brandgoogleomgWebWindows DesktopIEUSnonono20023030766201420142014183181181272727201
210716213446MALE32basic0ensem-brandgoogleomgWebMac DesktopSafariNDFyesnono0002930306662015201420141801811812727273640363
210717213447-unknown-34basic0endirectdirectlinkedWebWindows DesktopChromeNDFyesnono0002930306662015201420141801811812727273640363
210718213448-unknown-32basic0endirectdirectuntrackedWebMac DesktopFirefoxNDFyesnono0002930306662015201420141801811812727273640363
210719213449-unknown-34basic25enotherothertracked-otheriOSiPhoneMobile SafariNDFyesnono0002930306662015201420141801811812727273640363
210720213450-unknown-34basic25endirectdirectuntrackediOSiPhone-unknown-NDFyesnono0002930306662015201420141801811812727273640363